Three Fundamentals of the Biological Genetic Algorithm

作者: Stephen Freeland

DOI: 10.1007/978-1-4419-8983-3_19

关键词: Cognitive scienceMainstreamGenetic algorithmBiological evolutionEvolutionary theoryResearch findingsMasking (illustration)Evolutionary computation

摘要: Evolutionary computing began by lifting ideas from biological evolutionary theory into computer science, and continues to look toward new research findings for inspiration. However, an over enthusiastic ‘biology envy’ can only be the detriment of both disciplines masking broader potential two-way intellectual traffic shared insights analogizing one another. Three fundamental features evolution illustrate range flow between two communities: particulate genes carry some subtle consequences that have not yet translated mainstream EC; adaptive properties genetic code how communities contribute a common understanding appropriate abstractions; finally, EC exploration representational language seems pre-adapted help biologists understand why life evolved dichotomy genotype phenotype.

参考文章(30)
David A. Ostrowski, Robert G. Reynolds, Using Software Engineering Knowledge to Drive Genetic Program Design Using Cultural Algorithms Springer, Boston, MA. pp. 63- 80 ,(2003) , 10.1007/978-1-4419-8983-3_5
W. Banzhaf, Artificial Regulatory Networks and Genetic Programming Springer, Boston, MA. pp. 43- 61 ,(2003) , 10.1007/978-1-4419-8983-3_4
Stephen J. Freeland, The Darwinian Genetic Code: An Adaptation for Adapting? Genetic Programming and Evolvable Machines. ,vol. 3, pp. 113- 127 ,(2002) , 10.1023/A:1015527808424
Hillol Kargupta, A Striking Property of Genetic Code-like Transformations Complex Systems. ,vol. 13, ,(2001)
Eörs Szathmáry, John Maynard Smith, None, The Major Transitions in Evolution ,(1995)
M. A. Huynen, P. F. Stadler, W. Fontana, Smoothness within ruggedness: The role of neutrality in adaptation Proceedings of the National Academy of Sciences of the United States of America. ,vol. 93, pp. 397- 401 ,(1996) , 10.1073/PNAS.93.1.397